We describe here a comprehensive study on the effect of cellular structure and melt pool boundary (MPB) condition on the mechanical properties, deformation and failure behavior of AlSi10Mg alloy ...processed by selective laser melting (SLM). The morphology of melt pool (MP) on the load bearing face of tensile samples was significantly different with build directions. It resulted in different mechanical properties of the samples with different build directions. Furthermore, the microstructure analysis revealed that the MP in the SLM AlSi10Mg alloy mainly consisted of columnar α-Al grains which were made of ultra-fine elongated cellular structure. Electron back-scatter diffraction (EBSD) analysis revealed that the long axis of cellular structure and columnar grains were parallel to < 100 >, which resulted in < 100 > fiber texture in SLM AlSi10Mg alloy. However, Schmid factor calculation demonstrated that the anisotropy of mechanical properties of the SLM AlSi10Mg alloy built with different direction was mainly dependent on the distribution of MPB on the load bearing face, and not texture. The defects including pores, residual stress and heat affected zone (HAZ) located at MPB made it the weakest part in the SLM AlSi10Mg. The sample built along horizontal direction exhibited good combination of strength and plasticity and is attributed to the lowest fraction of MPBs that withstand load during tensile. MPB had strong influence on the mechanical properties and failure behavior of SLM AlSi10Mg built with different directions.
The International Society for Cell & Gene Therapy (ISCT®) Mesenchymal Stromal Cell (ISCT MSC) committee offers a position statement to clarify the nomenclature of mesenchymal stromal cells (MSCs). ...The ISCT MSC committee continues to support the use of the acronym “MSCs” but recommends this be (i) supplemented by tissue-source origin of the cells, which would highlight tissue-specific properties; (ii) intended as MSCs unless rigorous evidence for stemness exists that can be supported by both in vitro and in vivo data; and (iii) associated with robust matrix of functional assays to demonstrate MSC properties, which are not generically defined but informed by the intended therapeutic mode of actions.
MicroRNAs (miRNAs) act within Argonaute proteins to guide repression of messenger RNA targets. Although various approaches have provided insight into target recognition, the sparsity of miRNA-target ...affinity measurements has limited understanding and prediction of targeting efficacy. Here, we adapted RNA bind-n-seq to enable measurement of relative binding affinities between Argonaute-miRNA complexes and all sequences ≤12 nucleotides in length. This approach revealed noncanonical target sites specific to each miRNA, miRNA-specific differences in canonical target-site affinities, and a 100-fold impact of dinucleotides flanking each site. These data enabled construction of a biochemical model of miRNA-mediated repression, which was extended to all miRNA sequences using a convolutional neural network. This model substantially improved prediction of cellular repression, thereby providing a biochemical basis for quantitatively integrating miRNAs into gene-regulatory networks.
With the fast advance of connectome imaging techniques, we have the opportunity of mapping the human brain pathways in vivo at unprecedented resolution. In this article we review the current ...developments of diffusion magnetic resonance imaging (MRI) for the reconstruction of anatomical pathways in connectome studies. We first introduce the background of diffusion MRI with an emphasis on the technical advances and challenges in state-of-the-art multi-shell acquisition schemes used in the Human Connectome Project. Characterization of the microstructural environment in the human brain is discussed from the tensor model to the general fiber orientation distribution (FOD) models that can resolve crossing fibers in each voxel of the image. Using FOD-based tractography, we describe novel methods for fiber bundle reconstruction and graph-based connectivity analysis. Building upon these novel developments, there have already been successful applications of connectome imaging techniques in reconstructing challenging brain pathways. Examples including retinofugal and brainstem pathways will be reviewed. Finally, we discuss future directions in connectome imaging and its interaction with other aspects of brain imaging research.
Enthalpy release of electrodeposited nano-grained Ni and Ni-Mo alloys with grain size ranging from 29.3 to 5.9 nm and their corresponding annealed samples were investigated by using differential ...scanning calorimetry. Grain boundary energy of the annealed samples was estimated from enthalpy release and corresponding grain size. Results indicated that the enhanced grain boundary stability in the as-annealed ng Ni-Mo alloys is ascribed to kinetically grain boundary pinning by Mo instead of grain boundary energy reduction.
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Condensed matter systems have now become a fertile ground to discover emerging topological quasiparticles with symmetry protected modes. While many studies have focused on fermionic excitations, the ...same conceptual framework can also be applied to bosons yielding new types of topological states. Motivated by Zhang et al.'s recent theoretical prediction of double Weyl phonons in transition metal monosilicides Phys. Rev. Lett. 120, 016401 (2018)PRLTAO0031-900710.1103/PhysRevLett.120.016401, we directly measure the phonon dispersion in parity-breaking FeSi using inelastic x-ray scattering. By comparing the experimental data with theoretical calculations, we make the first observation of double Weyl points in FeSi, which will be an ideal material to explore emerging bosonic excitations and its topologically nontrivial properties.
In this paper, the impact damage of composite laminates in the form of intra- and inter-laminar cracking was modelled using stress-based criteria for damage initiation, and fracture mechanics ...techniques to capture its evolution. The nonlinear shear behaviour of the composite was described by the Soutis shear stress–strain semi-empirical formula. The finite element (FE) method was employed to simulate the behaviour of the composite under low velocity impact. Interface cohesive elements were inserted between plies with appropriate mixed-mode damage laws to model delamination. The damage model was implemented in the FE code (Abaqus/Explicit) by a user-defined material subroutine (VUMAT). Numerical results in general gave a good agreement when compared to experimentally obtained curves of impact force and absorbed energy versus time. The various damage mechanisms introduced during the impact event were observed by non-destructive technique (NDT) X-ray radiography and were successfully captured numerically by the proposed damage evolution model.
Circular RNAs (circRNAs) have been considered a special class of non-coding RNAs without 5′ caps and 3′ tails which are covalently closed RNA molecules generated by back splicing of mRNA. For a long ...time, circRNAs have been considered to be directly involved in various biological processes as functional RNA. In recent years, a variety of circRNAs have been found to have translational functions, and the resultant peptides also play biological roles in the emergence and progression of human disease. The discovery of these circRNAs and their encoded peptides has enriched genomics, helped us to study the causes of diseases, and promoted the development of biotechnology. The purpose of this review is to summarize the research progress of the detection methods, translation initiation mechanism, as well as functional mechanism of peptides encoded by circRNAs, with the goal of providing the directions for the discovery of biomarkers for diagnosis, prognosis, and therapeutic targets for human disease.
Complex-valued neural networks have many advantages over their real-valued counterparts. Conventional digital electronic computing platforms are incapable of executing truly complex-valued ...representations and operations. In contrast, optical computing platforms that encode information in both phase and magnitude can execute complex arithmetic by optical interference, offering significantly enhanced computational speed and energy efficiency. However, to date, most demonstrations of optical neural networks still only utilize conventional real-valued frameworks that are designed for digital computers, forfeiting many of the advantages of optical computing such as efficient complex-valued operations. In this article, we highlight an optical neural chip (ONC) that implements truly complex-valued neural networks. We benchmark the performance of our complex-valued ONC in four settings: simple Boolean tasks, species classification of an Iris dataset, classifying nonlinear datasets (Circle and Spiral), and handwriting recognition. Strong learning capabilities (i.e., high accuracy, fast convergence and the capability to construct nonlinear decision boundaries) are achieved by our complex-valued ONC compared to its real-valued counterpart.
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